Posterior simulation and Bayes factors in panel count data models

Abstract

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a parameterization in common use, and computation of marginal likelihoods and Bayes factors via Chib’s (1995) method is also considered. The methods are illustrated with two real data applications involving large samples and multiple random effects.

Abstract

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a parameterization in common use, and computation of marginal likelihoods and Bayes factors via Chib’s (1995) method is also considered. The methods are illustrated with two real data applications involving large samples and multiple random effects.

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